While it may seem that our industry lags in the technology race, a closer look reveals that we are closing critical gaps by focusing on the information these technologies generate. Machine learning and artificial intelligence (AI) generate a lot of buzz, but the true innovation is in understanding how to leverage data for more effective claims management, better outcomes, and reduced disability cost.
Decision support tools as the foundation for data intelligence
Data is no longer just a reporting element; it is the foundation for decision support tools (DSTs) such as claims automation, predictive modeling, and risk scoring tools. These tools transform data into actionable intelligence for more proactive management of claims and more strategic use of resources.
Imagine predicting the number of days a claimant will be off work on the day the claim is filed. Or knowing the likelihood it will go to the end of benefit (EOB) and transition to long-term disability (LTD). Consider the value of being able to quickly identify which claims are receiving the most appropriate treatments, and which can be automatically paid and closed, or “auto-adjudicated.”
More than 30% of short-term disability (STD) claims are related to normal pregnancies and simple surgeries, both well suited to auto-adjudication when using data and technology. This makes it possible to reduce claim loads by almost a third and essentially gives you a third more staff to focus on claims that are not as simple and can be more costly and complicated.
DSTs can help with all of this and more, such as helping determine which claims would benefit most from additional resources, including case managers, peer reviews, independent medical examinations, and so on. These limited, high-cost resources add cost to claims management, so they must be invested intelligently to yield outcomes that off-set the expense. We all want to keep simple claims on track to close and ensure that a low percentage of STD claims reach EOB, both being key performance indicators for most plan sponsors.
Some vendors assign a risk score for every claim, detailing on a dashboard which claims are likely to become challenges and which are likely to resolve without complications. Many plan sponsors do not utilize this simple and easy-to-implement tool. Instead, they accept a one-size-fits-all approach, spending resources on claims that could have been closed without intervention, while failing to identify claims that are slowly creeping off track.
We know the longer claimants are off work, the less likely they are to return. A study by IAIABC (The International Association of Industrial Accident Boards and Commissions) on workers’ compensation claims in manufacturing found that after only 12 weeks off work, there is a 50% chance of never returning, and after 52 weeks, the likelihood of return drops to almost zero. This shows us how critical it is to use one of the many risk-scoring tools available in the marketplace to allow an organization to focus critical resources on at-risk claims.
Despite the value data and technology can provide, a large study by Sanofi indicates that over 42% of Canadian health plan sponsors do not receive claim data, and only 2% pull the data from the carrier when available. Considering that data is likely the most valuable asset a vendor has, it’s hard to imagine most plan sponsors simply ignore it and/or do not actively apply it.
Improve your claim program with decision support tools
Today businesses generate massive transactional data. Transforming that raw data into actionable insights is one of the most important strategies a company can employ to gain and retain competitive advantage. Ask to access your data and find out how a decision support tool can improve your claim program to grow your business.
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This article originally appeared on @Work, the official publication of Disability Management Employer Coalition, in January 2020.
